Analysis of the Severity of Heterogeneity Protection Forest based on SVM and PCA

Authors

  • Ferzha Putra Utama
  • Arie Vatresia
  • Nanang Sugianto
  • Ulfah Nur Azizah

DOI:

https://doi.org/10.47839/ijc.23.3.3665

Keywords:

heterogeneity, forest, Semidang Bukit Kabu, support vector machine, principal component analysis, Bengkulu, Indonesia

Abstract

Forest heterogeneity indicates the forest condition on producing more carbon into environment. Semidang Bukit Kabu Hunting Park Forest is a nature reserve lies over two districts of Central Bengkulu and Seluma, Bengkulu Province, which should have a heterogeneous forest to protect its natural resources. However, the data showed that the condition of it does not appear to have dense forest heterogeneity anymore, and its rate still remain unknown. Remote sensing as one of tools to help the remote monitoring was believed to be helpful to this question. This study showed changes in the heterogeneity from 2016 to 2021. Sentinel-2 imageries were occupied to help the process of classification of forest and non-forest areas. Support Vector Machine, as one of powerful machine learning tools, was also help the process with the integrating of Principal Component Analysis to optimize forest characteristics. This study indicates that there are significant reductions of forest heterogeneity over the area. The number of forest (heterogeny areas) continues to decline from 8122 ha in 2016 to 4339 ha in 2021. Furthermore, this study had proven that the algorithm of support vector machines showed significant performance to build the model towards the data with overall accuracy rate of 0.9434 and a kappa index of 0.9833.

References

Y. Han, S. Gao, and C. Liu, “Evaluation and analysis of forest carbon sequestration and oxygen release value under cloud computing framework,” Procedia Comput. Sci., vol. 228, pp. 519–525, 2023, https://doi.org/10.1016/j.procs.2023.11.059.

M. Almaraz et al., “Dinitrogen emissions dominate nitrogen gas emissions from soils with low oxygen availability in a moist tropical forest,” J. Geophys. Res. Biogeosciences, vol. 128, no. 1, p. e2022JG007210, 2023, https://doi.org/10.1029/2022JG007210.

J. C. Habel, M. Teucher, P. Gros, V. Gfrerer, and J. Eberle, “The importance of dynamic open-canopy woodlands for the conservation of a specialist butterfly species,” Landsc. Ecol., vol. 37, no. 8, pp. 2121–2129, 2022, https://doi.org/10.1007/s10980-022-01472-2.

H. Y. S. H. Nugroho et al., “Mainstreaming ecosystem services from Indonesia’s remaining forests,” Sustain., vol. 14, no. 19, p. 12124, 2022, https://doi.org/10.3390/su141912124.

N. E. Lelana et al., “Bagworms in Indonesian plantation forests: Species composition, pest status, and factors that contribute to outbreaks,” Diversity, vol. 14, no. 6, p. 471, 2022, https://doi.org/10.3390/d14060471.

I. W. S. Dharmawan, N. M. Heriyanto, R. Garsetiasih, R. T. Kwatrina, and R. Sawitri, “The dynamics of vegetation structure, composition and carbon stock in peatland ecosystem of old secondary forest in Riau and South Sumatra provinces,” Land, vol. 13, no. 5, p. 663, 2024, https://doi.org/10.3390/land13050663.

S. Rahayu et al., “Functional trait profiles and diversity of trees regenerating in disturbed tropical forests and agroforests in Indonesia,” For. Ecosyst., vol. 9, p. 100030, 2022, https://doi.org/10.1016/j.fecs.2022.100030.

S. D. Hayati, I. Qayim, R. Raffiudin, N. S. Ariyanti, W. Priawandiputra, and M. Miftahudin, “Traditional knowledge of plants for Sunggau Rafters on three forest types for conservation of Apis dorsata in Indonesia,” Forests, vol. 15, no. 4, p. 657, 2024, https://doi.org/10.3390/f15040657.

A. B. Suwardi, Z. I. Navia, A. Mubarak, and M. Mardudi, “Diversity of home garden plants and their contribution to promoting sustainable livelihoods for local communities living near Serbajadi protected forest in Aceh Timur region, Indonesia,” Biol. Agric. Hortic., vol. 39, no. 3, pp. 170–182, 2023, https://doi.org/10.1080/01448765.2023.2182233.

S. Withaningsih, Parikesit, and R. Fadilah, “Diversity of bird species in Pangheotan grassland and Mount Masigit Kareumbi hunting park, West Java, Indonesia,” Biodiversitas, vol. 23, no. 6, pp. 2790–2798, 2022, https://doi.org/10.13057/biodiv/d230602.

Y. Nugroho et al., “Vegetation diversity, structure and composition of three forest ecosystems in Angsana coastal area, South Kalimantan, Indonesia,” Biodiversitas, vol. 23, no. 5, pp. 2640–2647, 2022, https://doi.org/10.13057/biodiv/d230547.

Y. Yang, J. Ma, H. Liu, L. Song, W. Cao, and Y. Ren, “Spatial heterogeneity analysis of urban forest ecosystem services in Zhengzhou City,” PLoS One, vol. 18, no. 6 June, pp. 1–27, 2023, https://doi.org/10.1371/journal.pone.0286800.

Q. Zaldo-Aubanell et al., “Environmental heterogeneity in human health studies. A compositional methodology for land use and land cover data,” Sci. Total Environ., vol. 806, 2022, https://doi.org/10.1016/j.scitotenv.2021.150308.

T. Merrick et al., “Unveiling spatial and temporal heterogeneity of a tropical forest canopy using high-resolution NIRv, FCVI, and NIRvrad from UAS observations,” Biogeosciences, vol. 18, no. 22, pp. 6077–6091, 2021, https://doi.org/10.5194/bg-18-6077-2021.

J. R. Matangaran, I. N. Anissa, Q. Adlan, and M. Mujahid, “Changes in floristic diversity and stand damage of tropical forests caused by logging operations in North Kalimantan, Indonesia,” Biodiversitas, vol. 23, no. 12, pp. 6358–6365, 2022, https://doi.org/10.13057/biodiv/d231233.

M. Dede, C. Asdak, and I. Setiawan, “Spatial dynamics model of land use and land cover changes: A comparison of CA, ANN, and ANN-CA,” Regist. J. Ilm. Teknol. Sist. Inf., vol. 8, no. 1, pp. 38–49, 2022, https://doi.org/10.26594/register.v8i1.2339.

H. Y. S. H. Nugroho et al., “Toward Water, energy, and food security in rural Indonesia: A review,” Water (Switzerland), vol. 14, no. 10, pp. 1–25, 2022, https://doi.org/10.3390/w14101645.

A. Umami, H. Sukmana, E. A. Wikurendra, and E. Paulik, “A review on water management issues: potential and challenges in Indonesia,” Sustain. Water Resour. Manag., vol. 8, no. 3, p. 63, 2022, https://doi.org/10.1007/s40899-022-00648-7.

A. Fosch et al., “Replanting unproductive palm oil with smallholder plantations can help achieve sustainable development goals in Sumatra, Indonesia,” Commun. Earth Environ., vol. 4, no. 1, pp. 1–12, 2023, https://doi.org/10.1038/s43247-023-01037-4.

H. Purnomo et al., “Public and private sector zero-deforestation commitments and their impacts: A case study from South Sumatra province, Indonesia,” Land use policy, vol. 134, no. July, p. 106818, 2023, https://doi.org/10.1016/j.landusepol.2023.106818.

N. Adani, Y. Subiakto, and S. Pranoto, “Structural mitigation of rob flood disaster through mangrove forest conservation in Indonesia coastal areas,” IOP Conf. Ser. Earth Environ. Sci., vol. 1173, no. 1, 2023, https://doi.org/10.1088/1755-1315/1173/1/012066.

P. Agarwal, D. Sahoo, Y. Parida, K. Ranjan Paltasingh, and J. Roy Chowdhury, “Land use changes and natural disaster fatalities: Empirical analysis for India,” Ecol. Indic., vol. 154, no. June, p. 110525, 2023, https://doi.org/10.1016/j.ecolind.2023.110525.

D. Rosalina et al., “Application of remote sensing and GIS for mapping changes in land area and mangrove density in the Kuri Caddi Mangrove tourism, South Sulawesi Province, Indonesia,” Biodiversitas, vol. 24, no. 2, pp. 1049–1056, 2023, doi: 10.13057/biodiv/d240246.

E. P. Yankovich, K. S. Yankovich, and N. V. Baranovskiy, “Dynamics of Forest Vegetation in an Urban Agglomeration Based on Landsat Remote Sensing Data for the Period 1990–2022: A Case Study,” Remote Sens., vol. 15, no. 7, 2023, https://doi.org/10.3390/rs15071935.

Md. O. Sarif and R. D. Gupta, “Spatiotemporal mapping of land use/land cover dynamics using remote sensing and GIS approach: a case study of Prayagraj City, India (1988–2018), Environ Dev Sustain, vol. 24, pp. 888–920, 2022. https://doi.org/10.1007/s10668-021-01475-0.

Purwanto, S. Latifah, Yonariza, F. Akhsani, E. I. Sofiana, and M. R. Ferdiansah, “Land cover change assessment using random forest and CA markov from remote sensing images in the protected forest of South Malang, Indonesia,” Remote Sens. Appl. Soc. Environ., vol. 32, p. 101061, 2023, https://doi.org/10.1016/j.rsase.2023.101061.

Z. Zafar, M. Zubair, Y. Zha, S. Fahd, and A. Ahmad Nadeem, “Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data,” Egypt. J. Remote Sens. Sp. Sci., vol. 27, no. 2, pp. 216–226, 2024, https://doi.org/10.1016/j.ejrs.2024.03.003.

R. Thakur and P. Panse, “Classification performance of land use from multispectral remote sensing images using decision tree, k-nearest neighbor, random forest and support vector machine using EuroSAT data,” Int. J. Intell. Syst. Appl. Eng., vol. 10, no. 1s, pp. 67–77, 2022.

M. P. Singh, V. Gayathri, and D. Chaudhuri, “A simple data preprocessing and postprocessing techniques for SVM classifier of remote sensing multispectral image classification,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 15, pp. 7248–7262, 2022, https://doi.org/10.1109/JSTARS.2022.3201273.

Z. A. Kakarash, H. S. Ezat, S. A. Omar, and N. F. Ahmed, “Time series forecasting based on support vector machine using particle swarm optimization,” Int. J. Comput., vol. 21, no. 1, pp. 76–88, 2022, https://doi.org/10.47839/ijc.21.1.2520.

R. Kosarevych, O. Lutsyk, B. Rusyn, O. Alokhina, T. Maksymyuk, and J. Gazda, “Spatial point patterns generation on remote sensing data using convolutional neural networks with further statistical analysis,” Sci. Rep., vol. 12, no. 1, pp. 1–9, 2022, https://doi.org/10.1038/s41598-022-18599-6.

A. Vatresia, F. Utama, N. Sugianto, A. Widyastiti, R. Rais, and R. Ismanto, “Automatic image segmentation model for indirect land use change with deep convolutional neural network,” Spat. Inf. Res., no. 0123456789, 2023, https://doi.org/10.1007/s41324-023-00560-y.

I. Ali, Z. Mushtaq, S. Arif, A. D. Algarni, N. F. Soliman, and W. El-Shafai, “Hyperspectral images-based crop classification scheme for agricultural remote sensing,” Comput. Syst. Sci. Eng., vol. 46, no. 1, pp. 303–319, 2023, https://doi.org/10.32604/csse.2023.034374.

R. Sugumar and D. Suganya, “A multi-spectral image-based high-level classification based on a modified SVM with enhanced PCA and hybrid metaheuristic algorithm,” Remote Sens. Appl. Soc. Environ., vol. 31, p. 100984, 2023, https://doi.org/10.1016/j.rsase.2023.100984.

K. F. Reich, M. Kunz, A. W. Bitter, and G. Von Oheimb, “Do different indices of forest structural heterogeneity yield consistent results?,” IForest, vol. 15, no. 5, pp. 424–432, 2022, https://doi.org/10.3832/ifor4096-015.

M. Greenacre, P. J. F. Groenen, T. Hastie, A. I. D’Enza, A. Markos, and E. Tuzhilina, “Principal component analysis,” Nat. Rev. Methods Prim., vol. 2, no. 1, p. 100, 2022, https://doi.org/10.1038/s43586-022-00184-w.

A. M. Simón Sánchez, J. González-Piqueras, L. de la Ossa, and A. Calera, “Convolutional Neural Networks for Agricultural Land Use Classification from Sentinel-2 Image Time Series,” Remote Sens., vol. 14, no. 21, 2022, https://doi.org/10.3390/rs14215373.

S. Yousefi et al., “Image classification and land cover mapping using Sentinel-2 imagery: Optimization of SVM parameters,” Land, vol. 11, no. 7, 2022, https://doi.org/10.3390/land11070993.

N. Baccari, “Evaluation of SVM and RF Machine Learning Algorithms in Land Use / Land Cover Change Assessment : Tessa Watershed Case Study (Northwest of Tunisia),” Appl. Geomatics, 2024, https://doi.org/10.21203/rs.3.rs-4359112/v1.

S. Basheer et al., “Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques,” Remote Sens., vol. 14, no. 19, pp. 1–18, 2022, https://doi.org/10.3390/rs14194978.

E. Piaser and P. Villa, “Comparing machine learning techniques for aquatic vegetation classification using Sentinel-2 data,” Proceedings of the 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), 2022, pp. 465–470, https://doi.org/10.1109/MELECON53508.2022.9843103.

C. Matyukira and P. Mhangara, “Land cover and landscape structural changes using extreme gradient boosting random forest and fragmentation analysis,” Remote Sens., vol. 15, no. 23, 2023, https://doi.org/10.3390/rs15235520.

A. Rash, Y. Mustafa, and R. Hamad, “Quantitative assessment of land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq,” Heliyon, vol. 9, no. 11, p. e21253, 2023, https://doi.org/10.1016/j.heliyon.2023.e21253.

A. Azedou, A. Amine, I. Kisekka, S. Lahssini, Y. Bouziani, and S. Moukrim, “Enhancing land cover/land use (LCLU) classification through a comparative analysis of hyperparameters optimization approaches for deep neural network (DNN),” Ecol. Inform., vol. 78, no. April, p. 102333, 2023, https://doi.org/10.1016/j.ecoinf.2023.102333.

Y. Ouma et al., “Comparison of machine learning classifiers for multitemporal and multisensor mapping of urban Lulc features,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 43, no. B3-2022, pp. 681–689, 2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-681-2022.

G. Doxani et al., “Atmospheric correction inter-comparison eXercise, ACIX-II Land: An assessment of atmospheric correction processors for Landsat 8 and Sentinel-2 over land,” Remote Sens. Environ., vol. 285, no. October 2022, 2023, https://doi.org/10.1016/j.rse.2022.113412.

P. Azinwi Tamfuh et al., “Mapping land use/land cover changes caused by mining activities from 2018 to 2022 using Sentinel-2 imagery in Bétaré-Oya (East-Cameroon),” J. Geosci. Geomatics, vol. 12, no. 1, pp. 12–23, 2024, https://doi.org/10.12691/jgg-12-1-3.

J. E. Ayala Izurieta et al., “Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel-2 and GIS using Gaussian processes regression,” Plant Soil, vol. 479, no. 1–2, pp. 159–183, 2022, https://doi.org/10.1007/s11104-022-05506-1.

A. Galodha, N. T. Ngoc, D. Raniwala, and S. Mundia, “Impact of coal mining, thermal plants, anthropogenic activities on wildlife corridors for national parks and wildlife sanctuaries in the state of Madhya Pradesh, India,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 46, no. M-2–2022, pp. 233–239, 2022, https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-233-2022.

K. Tempa and K. R. Aryal, “Semi-automatic classification for rapid delineation of the geohazard-prone areas using Sentinel-2 satellite imagery,” SN Appl. Sci., vol. 4, no. 5, 2022, https://doi.org/10.1007/s42452-022-05028-6.

F. Uhl, T. G. Rasmussen, and N. Oppelt, “Classification ensembles for beach cast and drifting vegetation mapping with Sentinel-2 and PlanetScope,” Geosci., vol. 12, no. 1, pp. 1–18, 2022, https://doi.org/10.3390/geosciences12010015.

G. S. Geethika, V. S. Sreeja, T. Tharuni, and V. Radhesyam, “Vegetation change detection of multispectral satellite images using remote sensing BT - High performance computing, smart devices and networks,” Proceedings of the Conference on Performance Computing, Smart Devices and Networks, 2024, pp. 337–349. https://doi.org/10.1007/978-981-99-6690-5_25.

A. Mullapudi, A. D. Vibhute, S. Mali, and C. H. Patil, “Spatial and seasonal change detection in vegetation cover using time-series landsat satellite images and machine learning methods,” SN Comput. Sci., vol. 4, no. 3, p. 254, 2023, https://doi.org/10.1007/s42979-023-01710-7.

K. He, “Pharmacological affinity fingerprints derived from bioactivity data for the identification of designer drugs,” J. Cheminform., vol. 14, no. 1, pp. 1–19, 2022, https://doi.org/10.1186/s13321-022-00607-6.

M. J. van Strien and A. Grêt-Regamey, “Unsupervised deep learning of landscape typologies from remote sensing images and other continuous spatial data,” Environ. Model. Softw., vol. 155, no. April 2021, 2022, https://doi.org/10.1016/j.envsoft.2022.105462.

E. M. Ordway et al., “Mapping tropical forest functional variation at satellite remote sensing resolutions depends on key traits,” Commun. Earth Environ., vol. 3, no. 1, pp. 1–11, 2022, https://doi.org/10.1038/s43247-022-00564-w.

X. Peng et al., “A Comparison of random forest algorithm-based forest extraction with GF-1 WFV, Landsat 8 and Sentinel-2 images,” Remote Sens., vol. 14, no. 21, p. 5296, 2022, https://doi.org/10.3390/rs14215296.

A. N. Rasyidah, I. S. Astuti, and I. Carolita, “Analysis of deforestation as impact of changes on oil palm land use in Tanah Bumbu Regency, South Kalimantan using satellite remote sensing data,” IOP Conf. Ser. Earth Environ. Sci., vol. 1066, no. 1, p. 012005, 2022, https://doi.org/10.1088/1755-1315/1066/1/012005.

M. Zickel, M. Gröbner, A. Röpke, and M. Kehl, “MiGIS: micromorphological soil and sediment thin section analysis using an open-source GIS and machine learning approach,” E G Quat. Sci. J., vol. 73, no. 1, pp. 69–93, 2024, https://doi.org/10.5194/egqsj-73-69-2024.

Y. Xi, A. M. Mohamed Taha, A. Hu, and X. Liu, “Accuracy comparison of various remote sensing data in lithological classification based on random forest algorithm,” Geocarto Int., vol. 37, no. 26, pp. 14451–14479, 2022, https://doi.org/10.1080/10106049.2022.2088859.

Z. Zhao et al., “The PCA-NDWI urban water extraction model based on hyperspectral remote sensing,” Water (Switzerland), vol. 16, no. 7, 2024, https://doi.org/10.3390/w16070963.

M. Mehmood, A. Shahzad, B. Zafar, A. Shabbir, and N. Ali, “Remote sensing image classification: A comprehensive review and applications,” Math. Probl. Eng., vol. 2022, 2022, https://doi.org/10.1155/2022/5880959.

G. Liu, L. Wang, D. Liu, L. Fei, and J. Yang, “Hyperspectral image classification based on non-parallel support vector machine,” Remote Sens., vol. 14, no. 10, pp. 1–22, 2022, https://doi.org/10.3390/rs14102447.

Y. Shang, X. Zheng, J. Li, D. Liu, and P. Wang, “A comparative analysis of swarm intelligence and evolutionary algorithms for feature selection in SVM-based hyperspectral image classification,” Remote Sens., vol. 14, no. 13, 2022, https://doi.org/10.3390/rs14133019.

A. Singleton, D. Arribas-Bel, J. Murray, and M. Fleischmann, “Estimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network,” Comput. Environ. Urban Syst., vol. 95, no. April, p. 101802, 2022, https://doi.org/10.1016/j.compenvurbsys.2022.101802.

Z. Bao et al., “Remote sensing-based assessment of ecosystem health by optimizing vigor-organization-resilience model: A case study in Fuzhou City, China,” Ecol. Inform., vol. 72, p. 101889, 2022, https://doi.org/10.1016/j.ecoinf.2022.101889.

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Published

2024-10-11

How to Cite

Utama, F. P., Vatresia, A., Sugianto, N., & Azizah, U. N. (2024). Analysis of the Severity of Heterogeneity Protection Forest based on SVM and PCA. International Journal of Computing, 23(3), 450-457. https://doi.org/10.47839/ijc.23.3.3665

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2024, Volume 23, Issue 3

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